mlsanityData to 14 Oct 2026

The Ecological Fallacy in Annotation: Modelling Human Label Variation goes beyond Sociodemographics

arXiv 2306.11559 ↗First posted 20 Jun 2023cs.CLcited by 46

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Abstract (arXiv)

Many NLP tasks exhibit human label variation, where different annotators give different labels to the same texts. This variation is known to depend, at least in part, on the sociodemographics of annotators. Recent research aims to model individual annotator behaviour rather than predicting aggregated labels, and we would expect that sociodemographic information is useful for these models. On the other hand, the ecological fallacy states that aggregate group behaviour, such as the behaviour of the average female annotator, does not necessarily explain individual behaviour. To account for sociodemographics in models of individual annotator behaviour, we introduce group-specific layers to multi-annotator models. In a series of experiments for toxic content detection, we find that explicitly accounting for sociodemographic attributes in this way does not significantly improve model performance. This result shows that individual annotation behaviour depends on much more than just sociodemographics.

Key facts. Cited by 46 papers in the map (data through 14 Oct 2026). Builds on 16 of its 49 references that are in the map. First posted on arXiv 20 Jun 2023.

Builds on

16 of 49 references.

References · 16

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  2. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  3. 2021Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotationscited by 314
  4. 2021Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detectioncited by 264
  5. 2021MultiVerS: Improving scientific claim verification with weak supervision and full-document contextcited by 143
  6. 2022The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluationcited by 139
  7. 2021On Releasing Annotator-Level Labels and Information in Datasetscited by 126
  8. 2022Jury Learning: Integrating Dissenting Voices into Machine Learning Modelscited by 118
  9. 2022Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotationcited by 69
  10. 2021Designing Toxic Content Classification for a Diversity of Perspectivescited by 65
  11. 2022Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluationcited by 59
  12. 2021ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AIcited by 46
4 more
  1. 2021Whose Opinions Matter? Perspective-aware Models to Identify Opinions of Hate Speech Victims in Abusive Language Detectioncited by 38
  2. 2017Like trainer, like bot? Inheritance of bias in algorithmic content moderationcited by 35
  3. 2017Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasetscited by 32
  4. 2021Towards Equal Gender Representation in the Annotations of Toxic Language Detectioncited by 9

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Citing papers per year: 2023: 4; 2024: 16; 2025: 11; 2026: 15.

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  1. 2024Quantifying the Persona Effect in LLM Simulationscited by 169
  2. 2023Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Promptingcited by 81
  3. 2024The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labelscited by 38
  4. 2025Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptionscited by 33
  5. 2023Capturing Perspectives of Crowdsourced Annotators in Subjective Learning Taskscited by 29
  6. 2025Value Profiles for Encoding Human Variationcited by 29
  7. 2024Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directionscited by 27
  8. 2024Hate Personified: Investigating the role of LLMs in content moderationcited by 25
  9. 2024Annotation alignment: Comparing LLM and human annotations of conversational safetycited by 23
  10. 2024Personas with Attitudes: Controlling LLMs for Diverse Data Annotationcited by 21
  11. 2024Annotator-Centric Active Learning for Subjective NLP Taskscited by 18
  12. 2024Position: Towards Bidirectional Human-AI Alignmentcited by 18
34 more
  1. 2023GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectivescited by 16
  2. 2026Beyond Consensus: Perspectivist Modeling and Evaluation of Annotator Disagreement in NLPcited by 13
  3. 2024D3CODE: Disentangling Disagreements in Data across Cultures on Offensiveness Detection and Evaluationcited by 11
  4. 2025Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectivescited by 10
  5. 2024Re-examining Sexism and Misogyny Classification with Annotator Attitudescited by 8
  6. 2024Robustness and Confounders in the Demographic Alignment of LLMs with Human Perceptions of Offensivenesscited by 7
  7. 2025NUTMEG: Separating Signal From Noise in Annotator Disagreementcited by 7
  8. 2025Evaluating how LLM annotations represent diverse views on contentious topicscited by 6
  9. 2023Architectural Sweet Spots for Modeling Human Label Variation by the Example of Argument Quality: It's Best to Relate Perspectives!cited by 5
  10. 2024Comparing Pre-trained Human Language Models: Is it Better with Human Context as Groups, Individual Traits, or Both?cited by 4
  11. 2026From Word Sequences to Behavioral Sequences: Adapting Modeling and Evaluation Paradigms for Longitudinal NLPcited by 4
  12. 2026Different Demographic Cues Yield Inconsistent Conclusions About LLM Personalization and Biascited by 4
  13. 2026LLMs Capture Emotion Labels, Not Emotion Uncertainty: Distributional Analysis and Calibration of Human-LLM Judgment Gapscited by 4
  14. 2024Let Guidelines Guide You: A Prescriptive Guideline-Centered Data Annotation Methodologycited by 3
  15. 2024Accurate and Data-Efficient Toxicity Prediction when Annotators Disagreecited by 3
  16. 2024SubData: Bridging Heterogeneous Datasets to Enable Theory-Driven Evaluation of Political and Demographic Perspectives in LLMscited by 3
  17. 2025Will Annotators Disagree? Identifying Subjectivity in Value-Laden Argumentscited by 3
  18. 2024Designing NLP Systems That Adapt to Diverse Worldviewscited by 2
  19. 2025Simulating Identity, Propagating Bias: Abstraction and Stereotypes in LLM-Generated Textcited by 2
  20. 2026From Ground Truth to Measurement: A Statistical Framework for Human Labelingcited by 2
  21. 2025Hateful Person or Hateful Model? Investigating the Role of Personas in Hate Speech Detection by Large Language Modelscited by 1
  22. 2026Modeling Human Perspectives with Socio-Demographic Representationscited by 1
  23. 2026Who and What? Using Linguistic Features and Annotator Characteristics to Analyze Annotation Variationcited by 1
  24. 2026Human Label Variation as Stable Signal: Learning Annotator-Specific Explanation Behavior via Cross-Annotator Preference Optimizationcited by 1
  25. 2025Balancing Quality and Variation: Spam Filtering Distorts Data Label Distributionscited by 0
  26. 2025Taking a SEAT: Predicting Value Interpretations from Sentiment, Emotion, Argument, and Topic Annotationscited by 0
  27. 2025Examining the Utility of Self-disclosure Types for Modeling Annotators of Social Normscited by 0
  28. 2026Not All Subjectivity Is the Same! Defining Desiderata for the Evaluation of Subjectivity in NLPcited by 0
  29. 2026When Does Demographic Information Help? Data and Modeling Regimes for Perspective-Aware Hate Speech Detectioncited by 0
  30. 2026From Self to Other: Evaluating Demographic Perspective-Taking in LLM Hate Speech Annotationcited by 0
  31. 2026Learning Perspectivist Social Meaning via Demographic-Conditioned Fusion Embeddingscited by 0
  32. 2026Demographic Prompting at Scale: When More Attributes Hurt LLM--Human Agreementcited by 0
  33. 2026PALMs: Using Multi Construct-Grounded Rationales for Modeling Population Preferences in LLMscited by 0
  34. 2026Decomposing and Predicting Annotation Disagreement in Harmful Content Across Languagescited by 0